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    ADAPTATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS BASED ON SITE-SPECIFIC DATA
    12.
    发明公开
    ADAPTATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS BASED ON SITE-SPECIFIC DATA 审中-公开

    公开(公告)号：US20240057021A1

    公开(公告)日：2024-02-15

    申请号：US18446320

    申请日：2023-08-08

    Applicant: QUALCOMM Incorporated

    Inventor： Hamed PEZESHKI ,  Taesang YOO ,  Tao LUO

    IPC: H04W64/00 ,  H04W72/044

    CPC classification number: H04W64/003 ,  H04W72/046

    Abstract: Systems and techniques for wireless communications are described herein. For example, a process for wireless communications at a first network entity include obtaining site-specific data associated with a geographic location and adapting, at the first network entity, a machine learning model based on the site-specific data to generate an updated machine learning model for estimating or predicting of at least one characteristic associated with wireless communications between the first network entity and one or more network entities. The first network entity can experience a trigger event which causes the first network entity to transmit a request for the site-specific data. The triggering event can be based on at least one of a location of the first network entity in the geographic location or the first network entity moving to the geographic location or based on other factors such as a change in a physical characteristic of the location.

    GRADIENT GROUPING FOR COMPRESSION IN FEDERATED LEARNING FOR MACHINE LEARNING MODELS
    16.
    发明公开
    GRADIENT GROUPING FOR COMPRESSION IN FEDERATED LEARNING FOR MACHINE LEARNING MODELS 审中-公开

    公开(公告)号：US20230325652A1

    公开(公告)日：2023-10-12

    申请号：US17714884

    申请日：2022-04-06

    Applicant: QUALCOMM Incorporated

    Inventor： Eren BALEVI ,  Taesang YOO

    IPC: G06N3/08 ,  H04W74/08

    CPC classification number: G06N3/08 ,  H04W74/0833

    Abstract: A method of wireless communication, by a user equipment (UE), includes receiving, from a network entity, a machine learning model for federated learning. The method also includes computing a set of gradient vector parameters during a first communication round of the federated learning for the machine learning model using a local dataset. The method further includes grouping the set of gradient vector parameters of the machine learning model into multiple subsets. The method also includes computing a representative value of all gradients within each of the subsets to obtain representative values for each of the subsets. The method includes transmitting the representative values to the network entity for the first communication round of the federated learning.

    PRECODING TECHNIQUES FOR WIRELESS COMMUNICATIONS
    17.
    发明公开
    PRECODING TECHNIQUES FOR WIRELESS COMMUNICATIONS 审中-公开

    公开(公告)号：US20230283332A1

    公开(公告)日：2023-09-07

    申请号：US18173482

    申请日：2023-02-23

    Applicant: QUALCOMM Incorporated

    Inventor： Saeid SAHRAEI ,  Gokul SRIDHARAN ,  Shimman Arvind PATEL ,  Krishna Kiran MUKKAVILLI ,  June NAMGOONG ,  Taesang YOO ,  Renqiu WANG

    IPC: H04B7/0456 ,  H04B7/06 ,  H04W72/23

    CPC classification number: H04B7/0465 ,  H04B7/0634 ,  H04W72/23

    Abstract: Methods, systems, and devices for wireless communications are described in which a base station may identify a null space matrix that lies within a null space of an effective channel matrix for communications between the base station and a user equipment (UE). An indication of the null space matrix may be provided to the UE, and the null space matrix used to determine modifications to a precoding matrix. The base station and UE may determine a redistribution matrix that provides a reduced variance of transmission powers for a number of transmission channels, where a product of the null space matrix and the redistribution matrix may be computed and added to the precoding matrix to generate a modified precoding matrix. The modified precoding matrix may be used to generate the communications from the base station and UE with reduced power variance across channels.

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